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Robust online motion capture labeling of finger markers
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0002-7801-7617
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH.ORCID iD: 0000-0003-1399-6604
2016 (English)In: Proceedings - Motion in Games 2016: 9th International Conference on Motion in Games, MIG 2016, ACM Digital Library, 2016, p. 7-13Conference paper, Published paper (Refereed)
Abstract [en]

Passive optical motion capture is one of the predominant technologies for capturing high fidelity human skeletal motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to fingers provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to the fingers of the hands. The method is especially suited for large capture volumes and sparse marker sets of 3 to 10 markers per hand. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). We evaluate the method on a collection of sparse marker sets commonly used in industry and in the research community. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications.

Place, publisher, year, edition, pages
ACM Digital Library, 2016. p. 7-13
Keywords [en]
Animation, Hand capture, Labeling, Motion capture, Online methods, Optical motion capture, Real-time application, Research communities, Skeletal motions, State-of-the-art system, Human computer interaction
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-201773DOI: 10.1145/2994258.2994264Scopus ID: 2-s2.0-84994806827ISBN: 9781450345927 (print)OAI: oai:DiVA.org:kth-201773DiVA, id: diva2:1075761
Conference
9th International Conference on Motion in Games, MIG 2016, 10 October 2016 through 12 October 2016
Note

Funding text: We wish to thank Anton Söderhäll and Samuel Tyskling at Imagination Studios for providing motion capture services used in this study as well as valuable feedback and discussions on the state-of-the-art in the industry. We also thank Ludovic Hoyet for discussions and for labeling the second data set with Vicon Blade. This work was funded by KTH/SRA ICT and The Next Generation and Science foundation Ireland PI grant S.F. 10/IN.1/13003.

QC 20170221

Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-21Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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